Build responsibly. These 6 lessons cover the real-world risks of LLM systems — prompt injection, jailbreaking, hallucinations, biases — and how to defend against them.
Prompt Injection: The Most Common AI Security Attack
Prompt injection tricks an AI into ignoring its instructions and following malicious commands embedded in user input or external data. Learn how it works and how to defend against it.
Prompt Leaking: Protecting Your System Prompts
Prompt leaking is when an AI is tricked into revealing its confidential system prompt. Learn why system prompts are hard to fully protect, what you can do, and what you should never put in one.
Jailbreaking: Techniques, Examples, and Defenses
Jailbreaking bypasses an AI's built-in safety guidelines through creative prompting. Learn the main jailbreak techniques, why they work, and how to make your AI systems more resistant to them.
Hallucinations Deep Dive: Why AI Confidently Gets Things Wrong
LLMs hallucinate — generating plausible-sounding but false information. Learn why hallucinations happen, which types of content are highest-risk, and practical techniques to minimize them.
Biases in LLM Outputs: What They Are and How to Reduce Them
LLMs inherit biases from training data, reinforcement feedback, and their own architecture. Learn the main bias types, how they surface in practice, and prompt strategies to reduce their impact.
Red-Teaming Your Prompts: Stress Test Before You Ship
Red-teaming is the practice of systematically attacking your own AI system to find vulnerabilities before real users do. Learn a practical red-teaming methodology for LLM applications.
Prerequisites
This track is most valuable after completing the Intermediate or Advanced track. Some lessons reference concepts like system prompts, chain-of-thought, and RAG.
Review Intermediate Track
